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Production responsiveness refers to the ability of a production system to achieve its operational goals in the presence of supplier, internal and customer disturbances, where disturbances are those sources of change which occur independently of the system’s intentions. A set of audit tools for assessing the responsiveness of production operations is being prepared as part of an EPSRC funded investigation. These tools are based on the idea that the ability to respond is linked to: the nature of the disturbances or changes requiring a response; their impact on production goals; and the inherent response capabilities of the operation. These response capabilities include information gathering and processing (to detect disturbances and production conditions), decision processes (which initiate system responses to disturbances) and various types of process flexibilities and buffers (which provide the physical means of dealing with disturbances). The paper discusses concepts and issues associated with production responsiveness, describes the audit tools that have been developed and illustrates their use in the context of a steel manufacturing plant.

A distinction must be drawn between a dismissal on the one hand, and on the other a repudiation of a contract of employment as a result of a breach of a fundamental term of that contract. When such a repudiation has been accepted by the innocent party then a termination of employment takes place. Such termination does not constitute dismissal (see London v. James Laidlaw & Sons Ltd (1974) IRLR 136 and Gannon v. J. C. Firth (1976) IRLR 415 EAT).

The development of the combined voltage reference and temperature sensor is focused on the RFID applications. The passive RFID systems derive power in the tag solely from rectifying the incident RF power. The dc power supply may be coupled with the RF signal, voltage drop, and noise. The voltage reference here is to provide a stable voltage for well‐biasing the internal analog circuitry. For the temperature sensing RFID applications, the combined device also gives a highly linear temperature sensor for wide‐temperature range measurements. Seeks to discuss this subject.

For voltage reference design, a self‐PTAT current is generated for compensating the diode‐connected NMOS transistor to achieve temperature‐stable voltage reference. Moreover, a temperature sensor with high linearity is developed by amplifying the linear portion and restricting the nonlinear part of temperature information.

Owing to better‐compensation, the voltage reference provides a stable voltage of 718.7±2.9 mV, and the temperature sensor has linearity over 99.8 percent for a wide‐temperature operation from −50 to 150°C.

Owing to the small size, 0.38 × 0.24 mm², of the combined device, it can be embedded into a RFID tag without increasing the RFID size. The voltage reference can serve as a stable voltage for stabilizing the behavior of analog circuits of the tag, and the temperature sensor probes the environment temperature. Then the information will be delivered to the RFID reader by the tag.

I. INTRODUCTION This study attempts to extend and expand previous research conducted by the Department of Marketing at Strathclyde on the adoption and diffusion of industrial products.

This research project focuses on developing techniques and technologies for automatically identifying human faces from images in the situations where face sample collections in the database as well as in the input query images are “as is”, i.e. no standard data collection environment is available. The developed method can also be used in other biometric applications.

The specific method presented in this paper is called scale independent identification (SII). SII allows direct “comparison” between two images in terms of whether the two objects (e.g. faces) in the two images are the same object (i.e. the same individual). SII is developed by extensively using the matrix computation theory and in particular, the singular value decomposition theory.

It is found that almost all the existing methods in the literature or technologies in the market require that a normalization in scale be done before any identification processing. However, it is also found that normalization in scale not only adds additional processing complexity, but also may reduce the identification accuracy. In addition, it is difficult to anticipate an “optimal” scale in advance. The developed SII complements the existing methods in all these aspects.

The only limitation which is also the limitation for many other biometric identification methods is that each object (e.g. individual in human face identification) must have a sufficient number of training samples collected before the method works well.

SII is particularly suitable in law enforcement and/or intelligence applications in which it is difficult or impossible to collect data in a standard, “clean” environment.

The SII method is new, and the paper should be interesting to researchers or engineers in this area, and should also be interesting to companies developing any biometrics‐based identification technologies as well as government agencies.

This paper discusses how corruption, in its various manifestations, has had — and continues to have — a very serious impact on the economic and political stability of the Asia‐Pacific region.

The paper aims to characterize anomaly detection in hyperspectral imagery.

This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image‐based computer simulations and real image experiments to substantiate the work proposed in this paper.

This paper developed an ACAD and custom‐designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are – first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation.

This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image‐based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

Radio frequency identification (RFID) is a technology for tracking objects that is expected to be widely adopted in very near future. A reader device sends probes to a set of RFID tags, which then respond to the request. A tag is recognized only when it is the only one to respond to the probe. Only reader has collision detection capability. The problem considered here is to minimize the number of probes necessary for reading all the tags, assuming that the number of tags is known in advance.

Well known binary and n‐ary partitioning algorithms can be applied to solve the problem for the case of known number of tags. A new randomized hybrid tag identification protocol has been proposed, which combines the two partitioning algorithms into a more efficient one. The new scheme optimizes the binary partition protocol for small values of n (e.g. n=2, 3, 4). The hybrid scheme then applies n‐ary partition protocol on the whole set, followed by binary partition on the tags that caused collision.

It is analytically proved that the expected number of time slots in the hybrid algorithm with known number of users is less than 2.20 n. Performance of these algorithms was also evaluated experimentally, and an improvement from en to approximately 2.15 n was obtained.

The algorithm shown here is efficient both by theory and practice and outperforms existing ones.

To design an efficient and integrated framework for automated and simple data acquisition and processing targeted for first response scenarios.

Utilizes existing software/hardware integration tools and primarily off‐the‐shelf components. Use the modular system architecture for development of new applications. System construction is preceded by the analysis of currently available devices for specific data acquisition and processing.

The development and integration of data acquisition and processing tools for first responder scenarios can be rapidly achieved by the modular and already existing software/hardware integration platform. Data types processed by this system are biometrics, live video/audio and textual/command data. The data acquisition is followed by the prompt dissemination of information from the incident scene thus overcoming interoperability issues.

Integration of new modules is achieved through simple system upgrades – new applications are created and integrated while the rest of the platform remains intact. Off‐the‐shelf components used eliminate the need for specialized hardware development. The speech user interface allows simple interaction with the system in an eyes‐off, hands‐off manner.

The system represents an efficient platform for integrated data acquisition and processing specially targeted for first response. The test‐bed flexibility allows for straightforward integration of devices/applications handling new data type as required by the user.

This paper describes the development of a novel automated vision system used to detect the visual defects on painted slates.

The vision system that has been developed consists of two major components covering the opto‐mechanical and algorithmical aspects of the system. The first component addresses issues including the mechanical implementation and interfacing the inspection system with the development of a fast image processing procedure able to identify visual defects present on the slate surface.

The inspection system was developed on 400 slates to determine the threshold settings that give the best trade‐off between no false positive triggers and correct defect identification. The developed system was tested on more than 300 fresh slates and the success rate for correct identification of acceptable and defective slates was 99.32 per cent for defect free slates based on 148 samples and 96.91 per cent for defective slates based on 162 samples.

The experimental data indicates that automating the inspection of painted slates can be achieved and installation in a factory is a realistic target. Testing the devised inspection system in a factory‐type environment was an important part of the development process as this enabled us to develop the mechanical system and the image processing algorithm able to perform slate inspection in an industrial environment. The overall performance of the system indicates that the proposed solution can be considered as a replacement for the existing manual inspection system.

The development of a real‐time automated system for inspecting painted slates proved to be a difficult task since the slate surface is dark coloured, glossy, has depth profile non‐uniformities and is being transported at high speeds on a conveyor. In order to address these issues, the system described in this paper proposed a number of novel solutions including the illumination set‐up and the development of multi‐component image‐processing inspection algorithm.